{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "\u003ca href=\"https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/keras_mnist_tpu.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TBsuwHGAv7w4"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Hub Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "pry0oCQUv7w8"
      },
      "outputs": [],
      "source": [
        "# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# =============================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xqLjB2cy5S7m"
      },
      "source": [
        "## MNIST on TPU (Tensor Processing Unit)\u003cbr\u003eor GPU using tf.Keras and tf.data.Dataset\n",
        "\u003ctable\u003e\u003ctr\u003e\u003ctd\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/keras-tensorflow-tpu300px.png\"   width=\"300\" alt=\"Keras+Tensorflow+Cloud TPU\"\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/table\u003e\n",
        "\n",
        "\n",
        "## Overview\n",
        "\n",
        "This sample trains an \"MNIST\" handwritten digit \n",
        "recognition model on a GPU or TPU backend using a Keras\n",
        "model. Data are handled using the tf.data.Datset API. This is\n",
        "a very simple sample provided for educational purposes. Do\n",
        "not expect outstanding TPU performance on a dataset as\n",
        "small as MNIST.\n",
        "\n",
        "This notebook is hosted on GitHub. To view it in its original repository, after opening the notebook, select **File \u003e View** on GitHub.\n",
        "\n",
        "## Learning objectives\n",
        "\n",
        "In this notebook, you will learn how to:\n",
        "*   Authenticate in Colab to access Google Cloud Storage (GSC)\n",
        "*   Format and prepare a dataset using tf.data.Dataset\n",
        "*   Create convolutional and dense layers using tf.keras.Sequential\n",
        "*   Build a Keras classifier with softmax, cross-entropy, and the adam optimizer\n",
        "*   Run training and validation in Keras using Cloud TPU\n",
        "*   Export a model for serving from ML Engine\n",
        "*   Deploy a trained model to ML Engine\n",
        "*  Test predictions on a deployed model\n",
        "\n",
        "## Instructions\n",
        "\n",
        "\u003ch3\u003e\u003ca href=\"https://cloud.google.com/gpu/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/gpu-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e  \u0026nbsp;\u0026nbsp;Train on GPU or TPU\u0026nbsp;\u0026nbsp; \u003ca href=\"https://cloud.google.com/tpu/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/tpu-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e\u003c/h3\u003e\n",
        "\n",
        "  1. Select a GPU or TPU backend (Runtime \u003e Change runtime type) \n",
        "  1. Runtime \u003e Run All \u003cbr/\u003e(Watch out: the \"Colab-only auth\" cell requires user input. \u003cbr/\u003eThe \"Deploy\" part at the end requires cloud project and bucket configuration.)\n",
        "\n",
        "\u003ch3\u003e\u003ca href=\"https://cloud.google.com/ml-engine/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/mlengine-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e  \u0026nbsp;\u0026nbsp;Deploy to AI Platform\u003c/h3\u003e\n",
        "At the bottom of this notebook you can deploy your trained model to AI Platform for a serverless, autoscaled, REST API experience. You will need a Google Cloud project and a GCS (Google Cloud Storage) bucket for this last part.\n",
        "\n",
        "TPUs are located in Google Cloud, for optimal performance, they read data directly from Google Cloud Storage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "AoilhmYe1b5t"
      },
      "outputs": [],
      "source": [
        "import os, re, time, json\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "qhdz68Xm3Z4Z"
      },
      "outputs": [],
      "source": [
        "#@title visualization utilities [RUN ME]\n",
        "\"\"\"\n",
        "This cell contains helper functions used for visualization\n",
        "and downloads only. You can skip reading it. There is very\n",
        "little useful Keras/Tensorflow code here.\n",
        "\"\"\"\n",
        "\n",
        "# Matplotlib config\n",
        "plt.rc('image', cmap='gray_r')\n",
        "plt.rc('grid', linewidth=0)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "\n",
        "# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
        "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
        "  \n",
        "  # get one batch from each: 10000 validation digits, N training digits\n",
        "  batch_train_ds = training_dataset.unbatch().batch(N)\n",
        "  \n",
        "  # eager execution: loop through datasets normally\n",
        "  if tf.executing_eagerly():\n",
        "    for validation_digits, validation_labels in validation_dataset:\n",
        "      validation_digits = validation_digits.numpy()\n",
        "      validation_labels = validation_labels.numpy()\n",
        "      break\n",
        "    for training_digits, training_labels in batch_train_ds:\n",
        "      training_digits = training_digits.numpy()\n",
        "      training_labels = training_labels.numpy()\n",
        "      break\n",
        "    \n",
        "  else:\n",
        "    v_images, v_labels = tf.compat.v1.data.make_one_shot_iterator(validation_dataset).get_next()\n",
        "    t_images, t_labels = tf.compat.v1.data.make_one_shot_iterator(batch_train_ds).get_next()\n",
        "    # Run once, get one batch. Session.run returns numpy results\n",
        "    with tf.Session() as ses:\n",
        "      (validation_digits, validation_labels,\n",
        "       training_digits, training_labels) = ses.run([v_images, v_labels, t_images, t_labels])\n",
        "  \n",
        "  # these were one-hot encoded in the dataset\n",
        "  validation_labels = np.argmax(validation_labels, axis=1)\n",
        "  training_labels = np.argmax(training_labels, axis=1)\n",
        "  \n",
        "  return (training_digits, training_labels,\n",
        "          validation_digits, validation_labels)\n",
        "\n",
        "# create digits from local fonts for testing\n",
        "def create_digits_from_local_fonts(n):\n",
        "  font_labels = []\n",
        "  img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
        "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
        "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
        "  d = PIL.ImageDraw.Draw(img)\n",
        "  for i in range(n):\n",
        "    font_labels.append(i%10)\n",
        "    d.text((7+i*28,0 if i\u003c10 else -4), str(i%10), fill=(255,255), font=font1 if i\u003c10 else font2)\n",
        "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
        "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
        "  return font_digits, font_labels\n",
        "\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_digits(digits, predictions, labels, title, n):\n",
        "  plt.figure(figsize=(13,3))\n",
        "  digits = np.reshape(digits, [n, 28, 28])\n",
        "  digits = np.swapaxes(digits, 0, 1)\n",
        "  digits = np.reshape(digits, [28, 28*n])\n",
        "  plt.yticks([])\n",
        "  plt.xticks([28*x+14 for x in range(n)], predictions)\n",
        "  for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
        "    if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
        "  plt.imshow(digits)\n",
        "  plt.grid(None)\n",
        "  plt.title(title)\n",
        "  \n",
        "# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
        "def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
        "  idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
        "  for i in range(lines):\n",
        "    display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
        "                   \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)\n",
        "    \n",
        "# utility to display training and validation curves\n",
        "def display_training_curves(training, validation, title, subplot):\n",
        "  if subplot%10==1: # set up the subplots on the first call\n",
        "    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
        "    plt.tight_layout()\n",
        "  ax = plt.subplot(subplot)\n",
        "  ax.grid(linewidth=1, color='white')\n",
        "  ax.plot(training)\n",
        "  ax.plot(validation)\n",
        "  ax.set_title('model '+ title)\n",
        "  ax.set_ylabel(title)\n",
        "  ax.set_xlabel('epoch')\n",
        "  ax.legend(['train', 'valid.'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VE6zp3q_HNTi"
      },
      "source": [
        "*(you can double-click on collapsed cells to view the non-essential code inside)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lzd6Qi464PsA"
      },
      "source": [
        "### Colab-only auth for this notebook and the TPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "MPx0nvyUnvgT"
      },
      "outputs": [],
      "source": [
        "IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ  # this is always set on Colab, the value is 0 or 1 depending on GPU presence\n",
        "if IS_COLAB_BACKEND:\n",
        "  from google.colab import auth\n",
        "  # Authenticates the Colab machine and also the TPU using your\n",
        "  # credentials so that they can access your private GCS buckets.\n",
        "  auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "R4jujVYWY9-6"
      },
      "source": [
        "### TPU or GPU detection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "colab_type": "code",
        "id": "Hd5zB1G7Y9-7",
        "outputId": "a2df453a-5477-4512-860c-93cf7bba6903"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on TPU  ['10.122.37.122:8470']\n",
            "Number of accelerators:  8\n"
          ]
        }
      ],
      "source": [
        "# Detect hardware\n",
        "try:\n",
        "  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection\n",
        "except ValueError:\n",
        "  tpu = None\n",
        "  gpus = tf.config.experimental.list_logical_devices(\"GPU\")\n",
        "    \n",
        "# Select appropriate distribution strategy\n",
        "if tpu:\n",
        "  tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "  strategy = tf.distribute.experimental.TPUStrategy(tpu, steps_per_run=128) # Going back and forth between TPU and host is expensive. Better to run 128 batches on the TPU before reporting back.\n",
        "  print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])  \n",
        "elif len(gpus) \u003e 1:\n",
        "  strategy = tf.distribute.MirroredStrategy([gpu.name for gpu in gpus])\n",
        "  print('Running on multiple GPUs ', [gpu.name for gpu in gpus])\n",
        "elif len(gpus) == 1:\n",
        "  strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
        "  print('Running on single GPU ', gpus[0].name)\n",
        "else:\n",
        "  strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n",
        "  print('Running on CPU')\n",
        "print(\"Number of accelerators: \", strategy.num_replicas_in_sync)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lvo0t7XVIkWZ"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "cCpkS9C_H7Tl"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 64 * strategy.num_replicas_in_sync # Gobal batch size.\n",
        "# The global batch size will be automatically sharded across all\n",
        "# replicas by the tf.data.Dataset API. A single TPU has 8 cores.\n",
        "# The best practice is to scale the batch size by the number of\n",
        "# replicas (cores). The learning rate should be increased as well.\n",
        "\n",
        "LEARNING_RATE = 0.01\n",
        "LEARNING_RATE_EXP_DECAY = 0.6 if strategy.num_replicas_in_sync == 1 else 0.7\n",
        "# Learning rate computed later as LEARNING_RATE * LEARNING_RATE_EXP_DECAY**epoch\n",
        "# 0.7 decay instead of 0.6 means a slower decay, i.e. a faster learnign rate.\n",
        "\n",
        "training_images_file   = 'gs://mnist-public/train-images-idx3-ubyte'\n",
        "training_labels_file   = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
        "validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
        "validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lz1Zknfk4qCx"
      },
      "source": [
        "### tf.data.Dataset: parse files and prepare training and validation datasets\n",
        "Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ZE8dgyPC1_6m"
      },
      "outputs": [],
      "source": [
        "def read_label(tf_bytestring):\n",
        "    label = tf.io.decode_raw(tf_bytestring, tf.uint8)\n",
        "    label = tf.reshape(label, [])\n",
        "    label = tf.one_hot(label, 10)\n",
        "    return label\n",
        "  \n",
        "def read_image(tf_bytestring):\n",
        "    image = tf.io.decode_raw(tf_bytestring, tf.uint8)\n",
        "    image = tf.cast(image, tf.float32)/255.0\n",
        "    image = tf.reshape(image, [28*28])\n",
        "    return image\n",
        "  \n",
        "def load_dataset(image_file, label_file):\n",
        "    imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
        "    imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
        "    labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
        "    labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
        "    dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
        "    return dataset \n",
        "  \n",
        "def get_training_dataset(image_file, label_file, batch_size):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache()  # this small dataset can be entirely cached in RAM\n",
        "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    dataset = dataset.batch(batch_size, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
        "    dataset = dataset.prefetch(-1)  # fetch next batches while training on the current one (-1: autotune prefetch buffer size)\n",
        "    return dataset\n",
        "  \n",
        "def get_validation_dataset(image_file, label_file):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache() # this small dataset can be entirely cached in RAM\n",
        "    dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    return dataset\n",
        "\n",
        "# instantiate the datasets\n",
        "training_dataset = get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_fXo6GuvL3EB"
      },
      "source": [
        "### Let's have a look at the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "colab_type": "code",
        "id": "yZ4tjPKvL2eh",
        "outputId": "f9af2fc9-e126-4a99-d1b2-b2b509804f01"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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xAhUVFQCA9957D7Nnz260ppTWrVtDR0cHCQkJ2LhxIz7//HMA4PNpfv3110hK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\nMBhNEGaoMxgMBoPBYDAYTZD/D0+Ub9ysWWabAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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/RKFeUFBATk5O4jX08PCgKVOm0JYtW2jLli3k4eEhru/iOE5SD7uQsvjgwYPN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\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "N = 24\n",
        "(training_digits, training_labels,\n",
        " validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
        "display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
        "display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
        "font_digits, font_labels = create_digits_from_local_fonts(N)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KIc0oqiD40HC"
      },
      "source": [
        "### Keras model: 3 convolutional layers, 2 dense layers\n",
        "If you are not sure what cross-entropy, dropout, softmax or batch-normalization mean, head here for a crash-course: [Tensorflow and deep learning without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/#featured-code-sample)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "colab_type": "code",
        "id": "56y8UNFQIVwj",
        "outputId": "c7a04a58-8829-47e6-c5b4-8fa75ba9d27f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "W0805 18:33:32.316657 140549664556928 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "image (Reshape)              (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 28, 28, 12)        108       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 28, 28, 12)        36        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 28, 28, 12)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 14, 14, 24)        10368     \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 14, 14, 24)        72        \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 14, 14, 24)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 7, 7, 32)          27648     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 7, 7, 32)          96        \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 200)               313600    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 200)               600       \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2010      \n",
            "=================================================================\n",
            "Total params: 354,538\n",
            "Trainable params: 354,002\n",
            "Non-trainable params: 536\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# This model trains to 99.4% accuracy in 10 epochs (with a batch size of 64)  \n",
        "\n",
        "def make_model():\n",
        "    model = tf.keras.Sequential(\n",
        "      [\n",
        "        tf.keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1), name=\"image\"),\n",
        "\n",
        "        tf.keras.layers.Conv2D(filters=12, kernel_size=3, padding='same', use_bias=False), # no bias necessary before batch norm\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True), # no batch norm scaling necessary before \"relu\"\n",
        "        tf.keras.layers.Activation('relu'), # activation after batch norm\n",
        "\n",
        "        tf.keras.layers.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
        "        tf.keras.layers.Activation('relu'),\n",
        "\n",
        "        tf.keras.layers.Conv2D(filters=32, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
        "        tf.keras.layers.Activation('relu'),\n",
        "\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(200, use_bias=False),\n",
        "        tf.keras.layers.BatchNormalization(scale=False, center=True),\n",
        "        tf.keras.layers.Activation('relu'),\n",
        "        tf.keras.layers.Dropout(0.4), # Dropout on dense layer only\n",
        "\n",
        "        tf.keras.layers.Dense(10, activation='softmax')\n",
        "      ])\n",
        "\n",
        "    model.compile(optimizer='adam', # learning rate will be set by LearningRateScheduler\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "    return model\n",
        "    \n",
        "with strategy.scope():\n",
        "    model = make_model()\n",
        "\n",
        "# print model layers\n",
        "model.summary()\n",
        "\n",
        "# set up learning rate decay\n",
        "lr_decay = tf.keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: LEARNING_RATE * LEARNING_RATE_EXP_DECAY**epoch,\n",
        "    verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CuhDh8ao8VyB"
      },
      "source": [
        "### Train and validate the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "colab_type": "code",
        "id": "TTwH_P-ZJ_xx",
        "outputId": "f40d5ea8-dd65-429b-81f7-55ef0ab9e26a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Steps per epoch:  117\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "W0805 18:33:41.028837 140549664556928 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_distributed.py:411: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Prefer Variable.assign which has equivalent behavior in 2.X.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: LearningRateScheduler reducing learning rate to 0.01.\n",
            "Epoch 1/10\n",
            "117/117 [==============================] - 5s 40ms/step - loss: 0.0258 - acc: 0.9449\n",
            "\n",
            "Epoch 00002: LearningRateScheduler reducing learning rate to 0.006999999999999999.\n",
            "Epoch 2/10\n",
            "117/117 [==============================] - 1s 9ms/step - loss: 0.0133 - acc: 0.9840\n",
            "\n",
            "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0049.\n",
            "Epoch 3/10\n",
            "117/117 [==============================] - 1s 8ms/step - loss: 0.0106 - acc: 0.9897\n",
            "\n",
            "Epoch 00004: LearningRateScheduler reducing learning rate to 0.003429999999999999.\n",
            "Epoch 4/10\n",
            "117/117 [==============================] - 1s 8ms/step - loss: 0.0100 - acc: 0.9924\n",
            "\n",
            "Epoch 00005: LearningRateScheduler reducing learning rate to 0.0024009999999999995.\n",
            "Epoch 5/10\n",
            "117/117 [==============================] - 1s 9ms/step - loss: 0.0094 - acc: 0.9946\n",
            "\n",
            "Epoch 00006: LearningRateScheduler reducing learning rate to 0.0016806999999999994.\n",
            "Epoch 6/10\n",
            "117/117 [==============================] - 1s 8ms/step - loss: 0.0156 - acc: 0.9963\n",
            "\n",
            "Epoch 00007: LearningRateScheduler reducing learning rate to 0.0011764899999999997.\n",
            "Epoch 7/10\n",
            "117/117 [==============================] - 1s 9ms/step - loss: 0.0134 - acc: 0.9972\n",
            "\n",
            "Epoch 00008: LearningRateScheduler reducing learning rate to 0.0008235429999999996.\n",
            "Epoch 8/10\n",
            "117/117 [==============================] - 1s 9ms/step - loss: 0.0105 - acc: 0.9976\n",
            "\n",
            "Epoch 00009: LearningRateScheduler reducing learning rate to 0.0005764800999999997.\n",
            "Epoch 9/10\n",
            "117/117 [==============================] - 1s 9ms/step - loss: 0.0041 - acc: 0.9979\n",
            "\n",
            "Epoch 00010: LearningRateScheduler reducing learning rate to 0.0004035360699999998.\n",
            "Epoch 10/10\n",
            "117/117 [==============================] - 1s 9ms/step - loss: 0.0039 - acc: 0.9981\n",
            "1/1 [==============================] - 4s 4s/step\n",
            "1/1 [==============================] - 4s 4s/step\n",
            "Validation accuracy:  0.99380004\n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "steps_per_epoch = 60000//BATCH_SIZE  # 60,000 items in this dataset\n",
        "print(\"Steps per epoch: \", steps_per_epoch)\n",
        "  \n",
        "# Little wrinkle: in the present version of Tensorfow (1.14), switching a TPU\n",
        "# between training and evaluation is slow (approx. 10 sec). For small models,\n",
        "# it is recommeneded to run a single eval at the end.\n",
        "history = model.fit(training_dataset,\n",
        "                    steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
        "                    callbacks=[lr_decay])\n",
        "\n",
        "final_stats = model.evaluate(validation_dataset, steps=1)\n",
        "print(\"Validation accuracy: \", final_stats[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "colab_type": "code",
        "id": "w12OId8Mz7dF",
        "outputId": "159289a9-f838-44ba-b973-bf2ba964af2e"
      },
      "outputs": [
        {
          "data": {
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lqta+wtQXL++zw0eQ/fBvNOzeDSqlEqrCQsgtLCAzMEDKmbN4fu6C8H827/bD0z1/IPv+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\nRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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JZACAoKAgrF27FjExMQCg8A5VZuLEibh9+zYAYO3atdDR0akx7vvA2rVrwePx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\nIiIiogDioE5EREREFEAc1ImIiIiIAug33ybo4aYktLcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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96S3ZgKqL9PR0LS3JcXUHe9Zvv5999hk4jtM6P6gpXjqhristo5QHHjUFP6uq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\nmFBnMBgMBoPBYDBaIf8Pp6Ei1gpuWIIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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bN9L7779Pc+bMoRMnTtCJEyeoe/fughjq+fn5DTY8UjXQ7e3tG41JV+5g2tYM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\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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66aevLTH48OFDPHz4EPv27atWsnH16tU66deFtLQ0tGvXDkQkiZ4mpaWlfAz5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\nMBgMBoPBYDAaIf8PfuLyRI2UmJcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Sm5srxPLyG2XwR+WBA394e3vrXBwkh1AHKuIT6ytwKwt1XV6ayjx8+FAr04uZ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\nMBgMBoPRBGFCncFgMBgMBoPBaIL8H/8+gmcy2cqRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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PD0pMTBRNKy4ujjQ1Nfnfzq5du2pVjswWk5aUlAAAnJ2dcebMGf64qqoqOnfu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\n5QAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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5y8PDwxEeHg6gZnahIc/6zp07m2U3PT0dhYWFWu//66+/SirUs7KykJWVhZEjR4KI0K9f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\nDfL/ASc4pqtCe5rFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# recognize digits from local fonts\n",
        "probabilities = model.predict(font_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_digits(font_digits, predicted_labels, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
        "\n",
        "# recognize validation digits\n",
        "probabilities = model.predict(validation_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5tzVi39ShrEL"
      },
      "source": [
        "## Deploy the trained model to AI Platform model serving\n",
        "\n",
        "Push your trained model to production on AI Platform for a serverless, autoscaled, REST API experience.\n",
        "\n",
        "You will need a GCS (Google Cloud Storage) bucket and a GCP project for this.\n",
        "Models deployed on AI Platform autoscale to zero if not used. There will be no AI Platform charges after you are done testing.\n",
        "Google Cloud Storage incurs charges. Empty the bucket after deployment if you want to avoid these. Once the model is deployed, the bucket is not useful anymore."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3Y3ztMY_toCP"
      },
      "source": [
        "### Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "iAZAn7yIhqAS"
      },
      "outputs": [],
      "source": [
        "PROJECT = \"\" #@param {type:\"string\"}\n",
        "BUCKET = \"gs://\"  #@param {type:\"string\", default:\"jddj\"}\n",
        "NEW_MODEL = True #@param {type:\"boolean\"}\n",
        "MODEL_NAME = \"mnist\" #@param {type:\"string\"}\n",
        "MODEL_VERSION = \"v1\" #@param {type:\"string\"}\n",
        "\n",
        "assert PROJECT, 'For this part, you need a GCP project. Head to http://console.cloud.google.com/ and create one.'\n",
        "assert re.search(r'gs://.+', BUCKET), 'For this part, you need a GCS bucket. Head to http://console.cloud.google.com/storage and create one.'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GxQTtjmdIbmN"
      },
      "source": [
        "### Export the model for serving from AI Platform"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "GOgh7Kb7SzzG",
        "outputId": "997b18e4-3fd9-451b-9eb0-328df9e4dac9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model exported to:  gs://ml1-demo-martin/keras_export/1565030988.5267901\n"
          ]
        }
      ],
      "source": [
        "# Wrap the model so that we can add a serving function\n",
        "class ExportModel(tf.keras.Model):\n",
        "  def __init__(self, model):\n",
        "    super().__init__(self)\n",
        "    self.model = model\n",
        "\n",
        "  # The serving function performig data pre- and post-processing.\n",
        "  # Pre-processing:  images are received in uint8 format converted\n",
        "  #                  to float32 before being sent to through the model.\n",
        "  # Post-processing: the Keras model outputs digit probabilities. We want\n",
        "  #                  the detected digits. An additional tf.argmax is needed.\n",
        "  # @tf.function turns the code in this function into a Tensorflow graph that\n",
        "  # can be exported. This way, the model itself, as well as its pre- and post-\n",
        "  # processing steps are exported in the SavedModel and deployed in a single step.\n",
        "  @tf.function(input_signature=[tf.TensorSpec([None, 28*28], dtype=tf.uint8)])\n",
        "  def my_serve(self, images):\n",
        "    images = tf.cast(images, tf.float32)/255   # pre-processing\n",
        "    probabilities = self.model(images)          # prediction from model\n",
        "    classes = tf.argmax(probabilities, axis=-1) # post-processing\n",
        "    return {'digits': classes}\n",
        "    \n",
        "# Must copy the model from TPU to CPU to be able to compose them.\n",
        "restored_model = make_model()\n",
        "restored_model.set_weights(model.get_weights()) # this copies the weights from TPU, does nothing on GPU\n",
        "\n",
        "# create the ExportModel and export it to the Tensorflow standard SavedModel format\n",
        "serving_model = ExportModel(restored_model)\n",
        "export_path = os.path.join(BUCKET, 'keras_export', str(time.time()))\n",
        "tf.keras.backend.set_learning_phase(0) # inference only\n",
        "tf.saved_model.save(serving_model, export_path, signatures={'serving_default': serving_model.my_serve})\n",
        "\n",
        "print(\"Model exported to: \", export_path)\n",
        "\n",
        "# Note: in Tensorflow 2.0, it will also be possible to\n",
        "# export to the SavedModel format using model.save():\n",
        "# serving_model.save(export_path, save_format='tf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "colab_type": "code",
        "id": "Zm7cpCRQC8-w",
        "outputId": "e74f04af-95c2-4e52-fa57-b5f1588c4f32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The given SavedModel contains the following tag-sets:\n",
            "serve\n",
            "The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:\n",
            "SignatureDef key: \"__saved_model_init_op\"\n",
            "SignatureDef key: \"serving_default\"\n",
            "The given SavedModel SignatureDef contains the following input(s):\n",
            "  inputs['images'] tensor_info:\n",
            "      dtype: DT_UINT8\n",
            "      shape: (-1, 784)\n",
            "      name: serving_default_images:0\n",
            "The given SavedModel SignatureDef contains the following output(s):\n",
            "  outputs['digits'] tensor_info:\n",
            "      dtype: DT_INT64\n",
            "      shape: (-1)\n",
            "      name: StatefulPartitionedCall:0\n",
            "Method name is: tensorflow/serving/predict\n"
          ]
        }
      ],
      "source": [
        "# saved_model_cli: a useful too for troubleshooting SavedModels (the tool is part of the Tensorflow installation)\n",
        "!saved_model_cli show --dir {export_path}\n",
        "!saved_model_cli show --dir {export_path} --tag_set serve\n",
        "!saved_model_cli show --dir {export_path} --tag_set serve --signature_def serving_default\n",
        "# A note on naming:\n",
        "# The \"serve\" tag set (i.e. serving functionality) is the only one exported by tf.saved_model.save\n",
        "# All the other names are defined by the user in the fllowing lines of code:\n",
        "#      def myserve(self, images):\n",
        "#                        ******\n",
        "#        return {'digits': classes}\n",
        "#                 ******\n",
        "#      tf.saved_model.save(..., signatures={'serving_default': serving_model.myserve})\n",
        "#                                            ***************"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zy3T3zk0u2J0"
      },
      "source": [
        "### Deploy the model\n",
        "This uses the command-line interface. You can do the same thing through the AI Platform UI at https://console.cloud.google.com/mlengine/models\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "nGv3ITiGLPL3"
      },
      "outputs": [],
      "source": [
        "# Create the model\n",
        "if NEW_MODEL:\n",
        "  !gcloud ai-platform models create {MODEL_NAME} --project={PROJECT} --regions=us-central1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "o3QtUowtOAL-"
      },
      "outputs": [],
      "source": [
        "# Create a version of this model (you can add --async at the end of the line to make this call non blocking)\n",
        "# Additional config flags are available: https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions\n",
        "# You can also deploy a model that is stored locally by providing a --staging-bucket=... parameter\n",
        "!echo \"Deployment takes a couple of minutes. You can watch your deployment here: https://console.cloud.google.com/mlengine/models/{MODEL_NAME}\"\n",
        "!gcloud ai-platform versions create {MODEL_VERSION} --model={MODEL_NAME} --origin={export_path} --project={PROJECT} --runtime-version=1.14 --python-version=3.5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jE-k1Zn6kU2Z"
      },
      "source": [
        "### Test the deployed model\n",
        "Your model is now available as a REST API. Let us try to call it. The cells below use the \"gcloud ml-engine\"\n",
        "command line tool but any tool that can send a JSON payload to a REST endpoint will work."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zZCt0Ke2QDer"
      },
      "outputs": [],
      "source": [
        "# prepare digits to send to online prediction endpoint\n",
        "digits_float32 = np.concatenate((font_digits, validation_digits[:100-N])) # pixel values in [0.0, 1.0] float range\n",
        "digits_uint8 = np.round(digits_float32*255).astype(np.uint8) # pixel values in [0, 255] int range\n",
        "labels = np.concatenate((font_labels, validation_labels[:100-N]))\n",
        "with open(\"digits.json\", \"w\") as f:\n",
        "  for digit in digits_uint8:\n",
        "    # the format for AI Platform online predictions is: one JSON object per line\n",
        "    data = json.dumps({\"images\": digit.tolist()})  # \"images\" because that was the name you gave this parametr in the serving funtion my_serve\n",
        "    f.write(data+'\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "colab_type": "code",
        "id": "n6PqhQ8RQ8bp",
        "outputId": "2b9c5d9f-d4ae-469b-ab27-6bdae237d0e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['DIGITS', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '1', '2', '3', '7', '2', '1', '0', '4', '1', '4', '9', '5', '9', '0', '6', '9', '0', '1', '5', '9', '7', '3', '4', '9', '6', '6', '5', '4', '0', '7', '4', '0', '1', '3', '1', '3', '4', '7', '2', '7', '1', '2', '1', '1', '7', '4', '2', '3', '5', '1', '2', '4', '4', '6', '3', '5', '5', '6', '0', '4', '1', '9', '5', '7', '8', '9', '3', '7', '4', '6', '4', '3', '0', '7', '0', '2', '9', '1', '7']\n"
          ]
        },
        {
          "data": {
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0igsL0aCJLRrYNQOgQv8g4uA4czpEYl3U09NTmn/8nj/gPGsm9GysIRLrwvHT6Ug5fQbF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\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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9ejRGjx7d6jlr164V/r9+/TqMjY1x69YtuLu7o6SkBAYGBgAALy8vuLq6alymloiPj4e+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\nDAaDwWB0QpiizmAwGAwGg8FgdEL+Hw4GI6EOvj93AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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paWn0xRdfkJOTEwGQpX48fPhQPJ7s7e0pKiqK7t+/TxqNhk6cOCE2doDKuz3X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\n6owxxhhjjBmg/weJOHLLBP/DPgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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sLCxE2yGTh3d34ZOcsekb4+7du9StWzehsjZ3x7e6iImJqXNTI96iHh8frxVP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\n",
            "text/plain": [
              "\u003cFigure size 936x216 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Request online predictions from deployed model (REST API) using the \"gcloud ml-engine\" command line.\n",
        "predictions = !gcloud ai-platform predict --model={MODEL_NAME} --json-instances digits.json --project={PROJECT} --version {MODEL_VERSION}\n",
        "print(predictions)\n",
        "\n",
        "predictions = np.stack([json.loads(p) for p in predictions[1:]]) # first elemet is the name of the output layer: drop it, parse the rest\n",
        "display_top_unrecognized(digits_float32, predictions, labels, N, 100//N)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2a5cGsSTEBQD"
      },
      "source": [
        "## What's next\n",
        "\n",
        "* Learn about [Cloud TPUs](https://cloud.google.com/tpu/docs) that Google designed and optimized specifically to speed up and scale up ML workloads for training and inference and to enable ML engineers and researchers to iterate more quickly.\n",
        "* Explore the range of [Cloud TPU tutorials and Colabs](https://cloud.google.com/tpu/docs/tutorials) to find other examples that can be used when implementing your ML project.\n",
        "\n",
        "On Google Cloud Platform, in addition to GPUs and TPUs available on pre-configured [deep learning VMs](https://cloud.google.com/deep-learning-vm/),  you will find [AutoML](https://cloud.google.com/automl/)*(beta)* for training custom models without writing code and [Cloud ML Engine](https://cloud.google.com/ml-engine/docs/) which will allows you to run parallel trainings and hyperparameter tuning of your custom models on powerful distributed hardware.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SVY1pBg5ydH-"
      },
      "source": [
        "## License"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hleIN5-pcr0N"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "author: Martin Gorner\u003cbr\u003e\n",
        "twitter: @martin_gorner\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    http://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "This is not an official Google product but sample code provided for an educational purpose\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [
        "TBsuwHGAv7w4"
      ],
      "name": "Keras MNIST TPU.ipynb",
      "provenance": [],
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
